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setup.py
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setup.py
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import os
import platform
import re
import subprocess
import sys
# from distutils.extension import Extension
# from Cython.Distutils import build_ext
from distutils.version import LooseVersion
from os.path import join as pjoin
import numpy as np
from setuptools import find_packages, setup, Extension
from setuptools.command.build_ext import build_ext
def find_in_path(name, path):
"Find a file in a search path"
# Adapted fom
# http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/
for dir in path.split(os.pathsep):
binpath = pjoin(dir, name)
if os.path.exists(binpath):
return os.path.abspath(binpath)
return None
def locate_cuda():
"""Locate the CUDA environment on the system
Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64'
and values giving the absolute path to each directory.
Starts by looking for the CUDAHOME env variable. If not found, everything
is based on finding 'nvcc' in the PATH.
"""
# first check if the CUDAHOME env variable is in use
if 'CUDAHOME' in os.environ:
home = os.environ['CUDAHOME']
nvcc = pjoin(home, 'bin', 'nvcc')
else:
# otherwise, search the PATH for NVCC
default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin')
nvcc = find_in_path('nvcc', os.environ['PATH'] + os.pathsep + default_path)
if nvcc is None:
raise EnvironmentError('The nvcc binary could not be '
'located in your $PATH. Either add it to your path, or set $CUDAHOME')
home = os.path.dirname(os.path.dirname(nvcc))
cudaconfig = {'home': home, 'nvcc': nvcc,
'include': pjoin(home, 'include'),
'lib64': pjoin(home, 'lib64')}
for k, v in cudaconfig.items():
if not os.path.exists(v):
raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v))
return cudaconfig
CUDA = locate_cuda()
# Obtain the numpy include directory. This logic works across numpy versions.
try:
numpy_include = np.get_include()
except AttributeError:
numpy_include = np.get_numpy_include()
def customize_compiler_for_nvcc(self):
"""inject deep into distutils to customize how the dispatch
to gcc/nvcc works.
If you subclass UnixCCompiler, it's not trivial to get your subclass
injected in, and still have the right customizations (i.e.
distutils.sysconfig.customize_compiler) run on it. So instead of going
the OO route, I have this. Note, it's kindof like a wierd functional
subclassing going on."""
# tell the compiler it can processes .cu
self.src_extensions.append('.cu')
# save references to the default compiler_so and _comple methods
default_compiler_so = self.compiler_so
super = self._compile
# now redefine the _compile method. This gets executed for each
# object but distutils doesn't have the ability to change compilers
# based on source extension: we add it.
def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts):
if os.path.splitext(src)[1] == '.cu':
# use the cuda for .cu files
self.set_executable('compiler_so', CUDA['nvcc'])
# use only a subset of the extra_postargs, which are 1-1 translated
# from the extra_compile_args in the Extension class
postargs = extra_postargs['nvcc']
else:
postargs = extra_postargs['gcc']
super(obj, src, ext, cc_args, postargs, pp_opts)
# reset the default compiler_so, which we might have changed for cuda
self.compiler_so = default_compiler_so
# inject our redefined _compile method into the class
self._compile = _compile
class CMakeExtension(Extension):
def __init__(self, name, sourcedir=''):
Extension.__init__(self, name, sources=[])
self.sourcedir = os.path.abspath(sourcedir)
# run the customize_compiler
class custom_build_ext(build_ext):
def build_extensions(self):
customize_compiler_for_nvcc(self.compiler)
build_ext.build_extensions(self)
def build_extension(self, ext):
if isinstance(ext, CMakeExtension):
# Check Cmake
try:
out = subprocess.check_output(['cmake', '--version'])
except OSError:
raise RuntimeError("CMake must be installed to build the following extensions: " +
", ".join(e.name for e in self.extensions))
if platform.system() == "Windows":
cmake_version = LooseVersion(re.search(r'version\s*([\d.]+)', out.decode()).group(1))
if cmake_version < '3.1.0':
raise RuntimeError("CMake >= 3.1.0 is required on Windows")
extdir = os.path.abspath(os.path.dirname(self.get_ext_fullpath(ext.name)))
cmake_args = ['-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=' + extdir,
'-DPYTHON_EXECUTABLE=' + sys.executable]
cfg = 'Debug' if self.debug else 'Release'
build_args = ['--config', cfg]
if platform.system() == "Windows":
cmake_args += ['-DCMAKE_LIBRARY_OUTPUT_DIRECTORY_{}={}'.format(cfg.upper(), extdir)]
if sys.maxsize > 2 ** 32:
cmake_args += ['-A', 'x64']
build_args += ['--', '/m']
else:
cmake_args += ['-DCMAKE_BUILD_TYPE=' + cfg]
build_args += ['--', '-j']
env = os.environ.copy()
env['CXXFLAGS'] = '{} -DVERSION_INFO=\\"{}\\"'.format(env.get('CXXFLAGS', ''),
self.distribution.get_version())
if not os.path.exists(self.build_temp):
os.makedirs(self.build_temp)
subprocess.check_call(['cmake', ext.sourcedir] + cmake_args, cwd=self.build_temp, env=env)
subprocess.check_call(['cmake', '--build', '.'] + build_args, cwd=self.build_temp)
else:
build_ext.build_extension(self, ext)
ext_modules = [
CMakeExtension('processing_cxx',
'operator_cxx/src_cxx')
]
setup(
name='rangedet',
version='1.0.0',
author='TuSimple',
description='An anchor-free LiDAR Detector purely based on the range view representation',
install_requires=['Cython', 'numpy'],
url='https://github.com/TuSimple/rangedet',
packages=find_packages(exclude=['config',
'datasets',
'imgs',
'tools',
'experiments',
'scripts']),
ext_modules=ext_modules,
cmdclass={'build_ext': custom_build_ext})